Clinical Significance of Preoperative Serum High Density Lipoprotein Cholesterol Levels in Soft Tissue Sarcoma
Bibliographic record
Abstract
The prognostic value of lipid profile remains unclear in soft tissue sarcoma. The aim of the present study was to validate the prognostic value of preoperative plasma lipid profile (high density lipoprotein-cholesterol [HDL-C], low density lipoprotein-cholesterol [LDL-C], cholesterol, and triglycerides) levels on disease-free survival (DFS) and overall survival (OS) in soft tissue sarcoma (STS) patients undergoing extensive and radical surgical resection.The preoperative plasma lipid profile levels of 234 STS patients, who were operated on between 2000 with 2010, were retrospectively evaluated. Kaplan-Meier curves and multivariate Cox proportional models were calculated for DFS and OS.In univariate analysis, a decreased HDL-C level was significantly associated with decreased OS (hazard ratio [HR], 3.405; 95% confidence interval (CI), 1.445-8.021, P = 0.005) and remained significant in the multivariate analysis (HR, 5.615; 95% CI, 1.243-25.378, P = 0.025). Patients with HDL-C < 1.475 mmol/L showed a median OS of 71 months. In contrast, patients with HDL-C ≥1.475 mmol/L had a median OS of 101 months. In univariate analysis, a decreased HDL-C level was significantly associated with decreased DFS (HR, 2.085; 95% CI, 1.271-3.422, P = 0.004) and remained significant in the multivariate analysis (HR, 1.808; 95% CI, 1.118-2.924, P = 0.016). Patients with HDL-C <1.475 mmol/L presented with a median DFS of 47 months, whereas patients with HDL-C ≥1.475 mmol/L had a median DFS of 78 months. In univariate analysis and multivariate analyses regarding OS and DFS, there was no significant association between the groups in terms of LDL-C, CHO and TG.Our study investigated the potential prognostic utility of preoperative plasma HDL-C levels as an independent factor in STS patients who had undergone radical surgical resection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".